INTRODUCTION - The seasonal Peakflow forecasts produced at the Northwest River Forecast Center are delivered through two products. The traditional Regression analysis uses the relationship forecasted seasonal volumes to peakflow. A second method incorporates Ensemble Streamflow Prediction (ESP) methodology using conceptual modeling.
REGRESSION MODEL: - The Northwest River Forecast Center (NWRFC) currently uses a statistical regression model to make water supply forecasts for the Columbia River Basin; the coastal streams of Washington and Oregon; and the Great Basin of Oregon. From January through June there are three forecasts produced every month. Each forecast has a varied amount of observed data available for model input and some diminution of forecast results may occur.
There is a strong relationship between seasonal volumes and the peakflows associated with those forecast volumes. The volumetric forecasts are based on precipitation reports from over 400 sites. Included in these procedures are snow water equivalent and observed runoff values from all available sites in Oregon; Washington; Idaho; Western Montana; Western Wyoming; Northern Nevada and British Columbia, Canada.
Simple linear and curvilinear relationships between volume and peakflows were then developed. Statistical analysis is performed to account for the most probable forecast and the associated uncertainty forecasts.
ESP MODEL: - The National Weather Service River Forecast System procedures use conceptual hydrologic and hydraulic models to simulate streamflow and reservoir conditions. An added component of the system is the Ensemble Prediction System (ESP). This procedure uses current conditions along with historical meteorological data and forecast meteorological data to make extended probabilistic forecasts. The forecasts are nothing but time series of future streamflow simulations.
The simulated streamflow traces can be scanned for different hydrologic parameters, such as max flow, min flow, volumes, etc., for a period in the future. Statistical analysis then produces a probabilistic forecast for each variable and period of interest. For peakflow we look at max flow and the date when the max flow occurs.
